Distributed training on consumer hardware is inherently chaotic - nodes crash, connections throttle, machines go dark mid-training. The real engineering challenge isn't preventing failures (impossible), it's how your framework handles them. Does it checkpoint aggressively? Can it rebalance workloads on the fly? Does it have smart retry logic or just fail the entire job? Most frameworks assume datacenter-grade reliability. Distributed systems built for consumer GPUs need fault tolerance baked into the core architecture, not bolted on as an afterthought. Failure recovery speed and job continuation without data loss are the metrics that actually matter here.